The general employee scheduling problem: an integration of MS and AI
Computers and Operations Research - Special issue: Applications of integer programming
Combinatorial optimization
Ant algorithms for discrete optimization
Artificial Life
A greedy genetic algorithm for the quadratic assignment problem
Computers and Operations Research
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Two-phases Method and Branch and Bound Procedures to Solve the Bi–objective Knapsack Problem
Journal of Global Optimization
Advances in Engineering Software
A new approach to solve hybrid flow shop scheduling problems by artificial immune system
Future Generation Computer Systems - Special issue: Computational science of lattice Boltzmann modelling
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Particle swarm optimization-based algorithms for TSP and generalized TSP
Information Processing Letters
Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment
Applied Soft Computing
A new ant colony optimization algorithm for the multidimensional Knapsack problem
Computers and Operations Research
A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
Computers and Operations Research
Multistart tabu search and diversification strategies for the quadratic assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Kernel search: A general heuristic for the multi-dimensional knapsack problem
Computers and Operations Research
Foundations of Algorithms using C++ Pseudocode, Third Edition
Foundations of Algorithms using C++ Pseudocode, Third Edition
Knapsack problem with probability constraints
Journal of Global Optimization
Journal of Global Optimization
Ant colony optimization for multiple knapsack problem and model bias
NAA'04 Proceedings of the Third international conference on Numerical Analysis and its Applications
Genetic algorithm based on the orthogonal design for multidimensional knapsack problems
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Apply the particle swarm optimization to the multidimensional knapsack problem
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simulated annealing and genetic algorithms for minimizing mean flow time in an open shop
Mathematical and Computer Modelling: An International Journal
A knapsack-type public key cryptosystem based on arithmetic in finite fields
IEEE Transactions on Information Theory - Part 1
Software section: MINTO, a mixed INTeger optimizer
Operations Research Letters
IEEE Transactions on Neural Networks
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The majority of Combinatorial Optimization Problems (COPs) are defined in the discrete space. Hence, proposing an efficient algorithm to solve the problems has become an attractive subject in recent years. In this paper, a meta-heuristic algorithm based on Binary Particle Swarm Algorithm (BPSO) and the governing Newtonian motion laws, so-called Binary Accelerated Particle Swarm Algorithm (BAPSA) is offered for discrete search spaces. The method is presented in two global and local topologies and evaluated on the 0---1 Multidimensional Knapsack Problem (MKP) as a famous problem in the class of COPs and NP-hard problems. Besides, the results are compared with BPSO for both global and local topologies as well as Genetic Algorithm (GA). We applied three methods of Penalty Function (PF) technique, Check-and-Drop (CD) and Improved Check-and-Repair Operator (ICRO) algorithms to solve the problem of infeasible solutions in the 0---1 MKP. Experimental results show that the proposed methods have better performance than BPSO and GA especially when ICRO algorithm is applied to convert infeasible solutions to feasible ones.